The level and nature of complexity is widely regarded as an important determinant of a number of economic, technological and organizational phenomena. A popular modeling tool for the representation of complexity in economics and organizational sciences is the NK model that represents the complexity stemming from the interactions among the elements of a system. This paper proposes an enhanced model for complexity that, though maintaining the core design (and properties) of the NK model, provides a more intuitive and richer representation of complexity, extending its applications and deepening the understanding of its effects on economic systems. The proposed pseudo-NK (pNK) model is defined on real-valued variables, as opposed to the binary variables required by NK, so as to allow for richer and more intuitive definitions of distance and search strategies. It also admits as a source of complexity not only the number of interactions, as in NK, but also their intensity, opening a novel way to express and measure the level of complexity. Finally, instead of relying on statistical properties of a large dataset of random values, pNK is defined as a deterministic function, far simpler to implement, to interpret and to calibrate for specific requirements. The paper replicates known results and presents original ones; in both cases, the proposed model proves a powerful tool for the investigation of the role of complexity, particularly in agent-based models. Copyright Springer-Verlag Berlin Heidelberg 2014
